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This paper presents a distributed online method for joint state and parameter estimation in a Jump Markov NonLinear System based on a distributed recursive Expectation Maximization algorithm. State inference is enabled via the use of Rao-Blackwellized Particle Filter and, for the parameter estimation, the E-step is performed independently at each sensor with the calculation of local sufficient statistics...
We describe new Bayesian algorithms for cooperative blind equalization in a network in which the signal broadcast by a single transmitter is received by multiple remote nodes through distinct frequency-selective channels. The algorithms are based on Rao-Blackwellized point mass filters, and approximate the posterior densities of the unknown channel parameters by Gaussian mixtures of fixed order. To...
We introduce in this paper the Random Exchange Diffusion Bernoulli Filter (RndEx-BF), which enables joint target detection and tracking by a network of collaborative sensors. RndEx-BF is a fully distributed algorithm that, unlike consensus-based solutions, does not require iterative internode communication between sensor measurements. Internode communication cost is further reduced by a novel hybrid...
We describe in this paper novel consensus-based distributed particle filtering algorithms which are applied to cooperative blind equalization of frequency-selective channels in a network with one transmitter and multiple receivers. The proposed algorithms employ parallel consensus averaging iterations to evaluate the product of some node-dependent quantities across the receiver network, thus eliminating...
We introduce in this paper a new distributed sequential Monte Carlo (SMC) algorithm for blind equalization of frequency-selective broadcast channels. In the considered setup, multiple receiving nodes sense independently distorted versions of the same broadcast signal and cooperate to recover it. The proposed approach innovates by using parametric approximations based on the Variational Bayes (VB)...
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